[1]} and [2]} converted the relation extraction task to a question-answering task and used the reading comprehension approach for zero-shot relation extraction. In this approach, entity 1 and the relation are used as questions, and entity 2 is the answer to the question. If entity 2 does not exist, the answer is NIL. [1]} used the BiDAF model [4]} with an additional NIL node in the output layer for this task on the WikiReading [5]} dataset with additional negative samples. They used a set of relations during training and another set of relations during testing. [2]} used templates to create the question using entity 1 and the relation. They modified the machine-reading comprehension models to a sequence tagging model so that they can find multiple answers to a question. Although they did not experiment with the zero-shot scenario, this approach can be used for zero-shot relation extraction too. FewRel 2.0 [7]} is a dataset for few-shot relation extraction. In few-shot relation extraction, training and test relations are different just like zero-shot extraction. But during testing, a few examples of the test relations are provided to the model for better prediction.
